Journal: IEEE Transactions on Intelligent Transportation Systems

Abbreviation

IEEE trans. intell. transp. syst.

Publisher

IEEE

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ISSN

1524-9050
1558-0016

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Publications 1 - 10 of 51
  • Corrections to "Integrated Urban Traffic Control for the Reduction of Travel Delays and Emissions'' [IEEE Trans. Intell. Transp. Syst., vol. 14, pp. 1609-1619, 2013.]
    Item type: Other Journal Item
    Jamshidnejad, Anahita; Lin, Shu; Xi, Yugeng; et al. (2019)
    IEEE Transactions on Intelligent Transportation Systems
  • Rescheduling trains using petri nets and heuristic search
    Item type: Journal Article
    Wang, Pengling; Ma, Lei; Goverde, Rob M.P.; et al. (2016)
    IEEE Transactions on Intelligent Transportation Systems
  • Visualizing the Relationship Between Human Mobility and Points of Interest
    Item type: Journal Article
    Zeng, Wei; Fu, Chi-Wing; Müller Arisona, Stefan; et al. (2017)
    IEEE Transactions on Intelligent Transportation Systems
  • Intermodal Autonomous Mobility-on-Demand
    Item type: Journal Article
    Salazar Villalon, Mauro; Lanzetti, Nicolas; Rossi, Federico; et al. (2020)
    IEEE Transactions on Intelligent Transportation Systems
    In this paper we study models and coordination policies for intermodal Autonomous Mobility-on-Demand (AMoD), wherein a fleet of self-driving vehicles provides on-demand mobility jointly with public transit. Specifically, we first present a network flow model for intermodal AMoD, where we capture the coupling between AMoD and public transit and the goal is to maximize social welfare. Second, leveraging such a model, we design a pricing and tolling scheme that allows the system to recover a social optimum under the assumption of a perfect market with selfish agents. Third, we present real-world case studies for the transportation networks of New York City and Berlin, which allow us to quantify the general benefits of intermodal AMoD, as well as the societal impact of different vehicles. In particular, we show that vehicle size and powertrain type heavily affect intermodal routing decisions and, thus, system efficiency. Our studies reveal that the cooperation between AMoD fleets and public transit can yield significant benefits compared to an AMoD system operating in isolation, whilst our proposed tolling policies appear to be in line with recent discussions for the case of New York City. © 2000-2011 IEEE.
  • Capability of current car-following models to reproduce vehicle free-flow acceleration dynamics
    Item type: Journal Article
    Ciuffo, Biagio; Makridis, Michail; Toledo, Tomer; et al. (2018)
    IEEE Transactions on Intelligent Transportation Systems
    Microscopic traffic simulation models are widely used to assess the impact of measures and technologies on the road transportation system. The assessment usually involves several measures of performance, such as overall traffic conditions, travel time, energy demand/fuel consumption, emissions, and safety. In doing so, it is usually assumed that traffic models are able to capture not only traffic dynamics but also vehicle dynamics (especially to compute energy/fuel consumption, emissions, and safety). However, this is not necessarily the case with the possibility of achieving unreliable outcomes when extrapolating from traffic to measures of performance related to the vehicle dynamics. The objective of the present paper is to assess the capability of existing car-following models to reproduce observed vehicle acceleration dynamics. A set of experiments was carried out in the Vehicle Emissions Laboratories of the European Commission Joint Research Centre in order to generate relevant data sets. These experiments are used to test the performance of three well-known car-following models. Although all models have been largely tested against their capability to correctly reproduce traffic dynamics, the findings raise concerns about their capability (and thus of the traffic models using them) to predict the effect on the microscopic vehicle dynamics and thus on emissions and energy/fuel consumption. The results of the present work can be considered valid beyond the analyzed car-following models, as simple acceleration rules are usually assumed in the vast majority of the traffic simulation frameworks. Consequently, it can be concluded that there is a number.
  • A Hybrid Optimal Control Approach to Fuel-Efficient Aircraft Conflict Avoidance
    Item type: Journal Article
    Soler, Manuel; Kamgarpour, Maryam; Lloret, Javier; et al. (2016)
    IEEE Transactions on Intelligent Transportation Systems
    We formulate fuel-optimal conflict-free aircraft trajectory planning as a hybrid optimal control problem. The discrete modes of the hybrid system capture the air traffic procedures for conflict resolution, e.g., speed and turn advisories. To solve problems of realistic dimensions arising from air traffic sector planning, we formulate a numerically tractable approach to solve the hybrid optimal control problem. The approach is based on introducing binary functions for each mode, relaxing the binary functions and including a penalty term on the relaxation. The transformed and discretized problem is a nonlinear program. We use the approach on a realistic case study with seven aircraft within an air traffic control sector, in which we find minimum-fuel conflict-free trajectories.
  • An Efficient Algorithm for Maximum Trajectory Coverage Query With Approximation Guarantee
    Item type: Journal Article
    He, Dan; Zhou, Thomas; Zhou, Xiaofang; et al. (2022)
    IEEE Transactions on Intelligent Transportation Systems
    In this paper, we study the $k$ Maximum Trajectory Coverage Query, which aims to find $k$ routes in a public transport system that can serve the maximum number of users with given journey trajectories. In existing studies, they only consider independent service that includes no transfers, but overlooks the aggregative service that includes transfer of multiple routes, resulting in inferior results. In our study, we consider both independent and aggregative services, which can help provide more meaningful results. However, the problem is NP-hard and non-submodular. To address this problem, we propose a greedy algorithm that iteratively selects the route with the maximum marginal gain considering both independent and aggregative services, and show that it outperforms the competitor by up to 60% in terms of accuracy. Since the problem is non-submodular, the greedy algorithm typically does not provide any approximation guarantee. By a mild assumption, we show that our proposed solution provides a constant approximation with respect to the optimal one. Moreover, since we need to consider both the independent and aggregative service, our greedy algorithm becomes more complicated and brings additional time overheads. To overcome such a deficiency, we further accelerate our solution using several heuristics and present an efficient method for spatially associating trajectories and routes. Extensive experiments on real-world datasets demonstrate that our optimisation brings up to an order of magnitude speedup and even outperforms existing solutions (that consider no aggregative service) by 2-3 times.
  • Connectivity Statistics of Store-and-Forward Intervehicle Communication
    Item type: Journal Article
    Kesting, Arne; Treiber, Martin; Helbing, Dirk (2010)
    IEEE Transactions on Intelligent Transportation Systems
  • Sampling-Based Path Planning in Highly Dynamic and Crowded Pedestrian Flow
    Item type: Journal Article
    Cai, Kuanqi; Chen, Weinan; Dugas, Daniel; et al. (2023)
    IEEE Transactions on Intelligent Transportation Systems
    Autonomous pedestrian-aware navigation in shared human-robot environments is a challenging problem. Here we consider a common situation in which a large crowd of pedestrians moves together in a limited space. Traditional planners struggle to find collision-free paths in such situations since the free space is limited and always changing. To solve this problem, we proposed a flow map-based RRT* method (FM-RRT*) containing a velocity layer and a minimally-intrusive layer. The proposed method models the velocity of the pedestrian flow and the area where the robot is less invasive to pedestrians. Furthermore, we propose an adaptive bias sampling, which drives the robot considering relative velocity, or minimal intrusion, according to the pedestrian flow. The evaluation is conducted in the Crowdbot Challenge simulator. The results show that our method can find a feasible path considering collision risk while simultaneously avoiding intrusive human movement.
  • An Unsupervised Approach for Inferring Driver Behavior From Naturalistic Driving Data
    Item type: Journal Article
    Bender, Asher); Agamennoni, Gabriel; Ward, James R.; et al. (2015)
    IEEE Transactions on Intelligent Transportation Systems
Publications 1 - 10 of 51